
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase_    :			List[Any]    =	2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowerCamelCase_    :			Dict    =	{
    # fairseq:
    """wmt19-ru-en""": {"""length_penalty""": 1.1},
    """wmt19-en-ru""": {"""length_penalty""": 1.15},
    """wmt19-en-de""": {"""length_penalty""": 1.0},
    """wmt19-de-en""": {"""length_penalty""": 1.1},
    # allenai:
    """wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
    """wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
    """wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}

# this remaps the different models to their organization names
lowerCamelCase_    :			int    =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowerCamelCase_    :			int    =	"""facebook"""
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
							lowerCamelCase_    :			Dict    =	"""allenai"""







def 							A__       (    lowerCamelCase    )	->						int:
				# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
				# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
				UpperCamelCase_:				Union[str, Any]						=    dict((re.sub(r"""@@$"""  ,     """"""  ,     lowerCamelCase    ), v) if k.endswith("""@@"""    ) else (re.sub(r"""$"""  ,     """</w>"""  ,     lowerCamelCase    ), v) for k, v in d.items()    )
				UpperCamelCase_:				Optional[Any]						=    """<s> <pad> </s> <unk>""".split()
				# restore the special tokens
				for k in keep_keys:
								del da[F'''{k}</w>''']
								UpperCamelCase_:				Any						=    d[k]  # restore
				return da







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[int]:
				# prep
				assert os.path.exists(lowerCamelCase    )
				os.makedirs(lowerCamelCase  ,     exist_ok=lowerCamelCase    )
				print(F'''Writing results to {pytorch_dump_folder_path}'''    )

				# handle various types of models

				UpperCamelCase_:				Tuple						=    basename(lowerCamelCase    )
				UpperCamelCase_:				List[Any]						=    dirname(lowerCamelCase    )

				UpperCamelCase_:				Union[str, Any]						=    fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
				UpperCamelCase_:				str						=    cls.hub_models()
				UpperCamelCase_:				Optional[Any]						=    {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
				UpperCamelCase_:				Tuple						=    """."""
				# note: since the model dump is old, fairseq has upgraded its model some
				# time later, and it does a whole lot of rewrites and splits on the saved
				# weights, therefore we can't use torch.load() directly on the model file.
				# see: upgrade_state_dict(state_dict) in fairseq_model.py
				print(F'''using checkpoint {checkpoint_file}'''    )
				UpperCamelCase_:				Union[str, Any]						=    hub_utils.from_pretrained(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     archive_map=lowerCamelCase  ,     **lowerCamelCase    )

				UpperCamelCase_:				Optional[int]						=    vars(chkpt["""args"""]["""model"""]    )

				UpperCamelCase_:				Dict						=    args["""source_lang"""]
				UpperCamelCase_:				Dict						=    args["""target_lang"""]

				UpperCamelCase_:				List[Any]						=    dirname(lowerCamelCase    )
				UpperCamelCase_:				Dict						=    basename(lowerCamelCase    )

				# dicts
				UpperCamelCase_:				int						=    os.path.join(lowerCamelCase  ,     F'''dict.{src_lang}.txt'''    )
				UpperCamelCase_:				List[str]						=    os.path.join(lowerCamelCase  ,     F'''dict.{tgt_lang}.txt'''    )

				UpperCamelCase_:				Optional[Any]						=    Dictionary.load(lowerCamelCase    )
				UpperCamelCase_:				Any						=    rewrite_dict_keys(src_dict.indices    )
				UpperCamelCase_:				Dict						=    len(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    os.path.join(lowerCamelCase  ,     """vocab-src.json"""    )
				print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''    )
				with open(lowerCamelCase  ,     """w"""  ,     encoding="""utf-8"""    ) as f:
								f.write(json.dumps(lowerCamelCase  ,     ensure_ascii=lowerCamelCase  ,     indent=lowerCamelCase    )    )

				# detect whether this is a do_lower_case situation, which can be derived by checking whether we
				# have at least one uppercase letter in the source vocab
				UpperCamelCase_:				Dict						=    True
				for k in src_vocab.keys():
								if not k.islower():
												UpperCamelCase_:				str						=    False
												break

				UpperCamelCase_:				Tuple						=    Dictionary.load(lowerCamelCase    )
				UpperCamelCase_:				Tuple						=    rewrite_dict_keys(tgt_dict.indices    )
				UpperCamelCase_:				str						=    len(lowerCamelCase    )
				UpperCamelCase_:				Optional[int]						=    os.path.join(lowerCamelCase  ,     """vocab-tgt.json"""    )
				print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''    )
				with open(lowerCamelCase  ,     """w"""  ,     encoding="""utf-8"""    ) as f:
								f.write(json.dumps(lowerCamelCase  ,     ensure_ascii=lowerCamelCase  ,     indent=lowerCamelCase    )    )

				# merges_file (bpecodes)
				UpperCamelCase_:				Tuple						=    os.path.join(lowerCamelCase  ,     VOCAB_FILES_NAMES["""merges_file"""]    )
				for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
								UpperCamelCase_:				Union[str, Any]						=    os.path.join(lowerCamelCase  ,     lowerCamelCase    )
								if os.path.exists(lowerCamelCase    ):
												break
				with open(lowerCamelCase  ,     encoding="""utf-8"""    ) as fin:
								UpperCamelCase_:				List[Any]						=    fin.read()
				UpperCamelCase_:				Union[str, Any]						=    re.sub(r""" \d+$"""  ,     """"""  ,     lowerCamelCase  ,     0  ,     re.M    )  # remove frequency number
				print(F'''Generating {merges_file}'''    )
				with open(lowerCamelCase  ,     """w"""  ,     encoding="""utf-8"""    ) as fout:
								fout.write(lowerCamelCase    )

				# model config
				UpperCamelCase_:				List[str]						=    os.path.join(lowerCamelCase  ,     """config.json"""    )

				# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
				# may have to modify the tokenizer if a different type is used by a future model
				assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args["bpe"]}'''
				assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args["tokenizer"]}'''

				UpperCamelCase_:				Dict						=    {
				    """architectures""": ["""FSMTForConditionalGeneration"""],
				    """model_type""": """fsmt""",
				    """activation_dropout""": args["""activation_dropout"""],
				    """activation_function""": """relu""",
				    """attention_dropout""": args["""attention_dropout"""],
				    """d_model""": args["""decoder_embed_dim"""],
				    """dropout""": args["""dropout"""],
				    """init_std""": 0.02,
				    """max_position_embeddings""": args["""max_source_positions"""],
				    """num_hidden_layers""": args["""encoder_layers"""],
				    """src_vocab_size""": src_vocab_size,
				    """tgt_vocab_size""": tgt_vocab_size,
				    """langs""": [src_lang, tgt_lang],
				    """encoder_attention_heads""": args["""encoder_attention_heads"""],
				    """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
				    """encoder_layerdrop""": args["""encoder_layerdrop"""],
				    """encoder_layers""": args["""encoder_layers"""],
				    """decoder_attention_heads""": args["""decoder_attention_heads"""],
				    """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
				    """decoder_layerdrop""": args["""decoder_layerdrop"""],
				    """decoder_layers""": args["""decoder_layers"""],
				    """bos_token_id""": 0,
				    """pad_token_id""": 1,
				    """eos_token_id""": 2,
				    """is_encoder_decoder""": True,
				    """scale_embedding""": not args["""no_scale_embedding"""],
				    """tie_word_embeddings""": args["""share_all_embeddings"""],
				}

				# good hparam defaults to start with
				UpperCamelCase_:				Union[str, Any]						=    5
				UpperCamelCase_:				Optional[Any]						=    False
				if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
								UpperCamelCase_:				Dict						=    best_score_hparams[model_dir]["""length_penalty"""]
				else:
								UpperCamelCase_:				List[Any]						=    1.0

				print(F'''Generating {fsmt_model_config_file}'''    )
				with open(lowerCamelCase  ,     """w"""  ,     encoding="""utf-8"""    ) as f:
								f.write(json.dumps(lowerCamelCase  ,     ensure_ascii=lowerCamelCase  ,     indent=lowerCamelCase    )    )

				# tokenizer config
				UpperCamelCase_:				Union[str, Any]						=    os.path.join(lowerCamelCase  ,     lowerCamelCase    )

				UpperCamelCase_:				List[Any]						=    {
				    """langs""": [src_lang, tgt_lang],
				    """model_max_length""": 10_24,
				    """do_lower_case""": do_lower_case,
				}

				print(F'''Generating {fsmt_tokenizer_config_file}'''    )
				with open(lowerCamelCase  ,     """w"""  ,     encoding="""utf-8"""    ) as f:
								f.write(json.dumps(lowerCamelCase  ,     ensure_ascii=lowerCamelCase  ,     indent=lowerCamelCase    )    )

				# model
				UpperCamelCase_:				Optional[int]						=    chkpt["""models"""][0]
				UpperCamelCase_:				List[Any]						=    model.state_dict()

				# rename keys to start with 'model.'
				UpperCamelCase_:				Optional[Any]						=    OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()    )

				# remove unneeded keys
				UpperCamelCase_:				List[str]						=    [
				    """model.model""",
				    """model.encoder.version""",
				    """model.decoder.version""",
				    """model.encoder_embed_tokens.weight""",
				    """model.decoder_embed_tokens.weight""",
				    """model.encoder.embed_positions._float_tensor""",
				    """model.decoder.embed_positions._float_tensor""",
				]
				for k in ignore_keys:
								model_state_dict.pop(lowerCamelCase  ,     lowerCamelCase    )

				UpperCamelCase_:				int						=    FSMTConfig.from_pretrained(lowerCamelCase    )
				UpperCamelCase_:				Optional[int]						=    FSMTForConditionalGeneration(lowerCamelCase    )

				# check that it loads ok
				model_new.load_state_dict(lowerCamelCase  ,     strict=lowerCamelCase    )

				# save
				UpperCamelCase_:				List[str]						=    os.path.join(lowerCamelCase  ,     lowerCamelCase    )
				print(F'''Generating {pytorch_weights_dump_path}'''    )
				torch.save(lowerCamelCase  ,     lowerCamelCase    )

				print("""Conversion is done!"""    )
				print("""\nLast step is to upload the files to s3"""    )
				print(F'''cd {data_root}'''    )
				print(F'''transformers-cli upload {model_dir}'''    )


if __name__ == "__main__":
							lowerCamelCase_    :			Union[str, Any]    =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--fsmt_checkpoint_path""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
							        """ bpecodes, etc."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_    :			Optional[int]    =	parser.parse_args()
							convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)








import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=()  ,     lowerCamelCase=None  ,     lowerCamelCase="no"  ,     lowerCamelCase="29500"    )	->						List[Any]:
				UpperCamelCase_:				Any						=    False
				UpperCamelCase_:				int						=    False
				if any(key.startswith("""KAGGLE"""    ) for key in os.environ.keys()    ):
								UpperCamelCase_:				str						=    True
				elif "IPython" in sys.modules:
								UpperCamelCase_:				Tuple						=    """google.colab""" in str(sys.modules["""IPython"""].get_ipython()    )

				try:
								UpperCamelCase_:				Union[str, Any]						=    PrecisionType(mixed_precision.lower()    )
				except ValueError:
								raise ValueError(
								    F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''    )

				if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""  ,     lowerCamelCase    ) is not None):
								# TPU launch
								import torch_xla.distributed.xla_multiprocessing as xmp

								if len(AcceleratorState._shared_state    ) > 0:
												raise ValueError(
												    """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
												    """your training function. Restart your notebook and make sure no cells initializes an """
												    """`Accelerator`."""    )
								if num_processes is None:
												UpperCamelCase_:				str						=    8

								UpperCamelCase_:				int						=    PrepareForLaunch(lowerCamelCase  ,     distributed_type="""TPU"""    )
								print(F'''Launching a training on {num_processes} TPU cores.'''    )
								xmp.spawn(lowerCamelCase  ,     args=lowerCamelCase  ,     nprocs=lowerCamelCase  ,     start_method="""fork"""    )
				elif in_colab:
								# No need for a distributed launch otherwise as it's either CPU or one GPU.
								if torch.cuda.is_available():
												print("""Launching training on one GPU."""    )
								else:
												print("""Launching training on one CPU."""    )
								function(*lowerCamelCase    )
				else:
								if num_processes is None:
												raise ValueError(
												    """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""    )

								if num_processes > 1:
												# Multi-GPU launch
												from torch.multiprocessing import start_processes
												from torch.multiprocessing.spawn import ProcessRaisedException

												if len(AcceleratorState._shared_state    ) > 0:
																raise ValueError(
																    """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
																    """inside your training function. Restart your notebook and make sure no cells initializes an """
																    """`Accelerator`."""    )

												if torch.cuda.is_initialized():
																raise ValueError(
																    """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
																    """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
																    """function."""    )

												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=lowerCamelCase  ,     master_addr="""127.0.01"""  ,     master_port=lowerCamelCase  ,     mixed_precision=lowerCamelCase    ):
																UpperCamelCase_:				Optional[int]						=    PrepareForLaunch(lowerCamelCase  ,     distributed_type="""MULTI_GPU"""    )
																print(F'''Launching training on {num_processes} GPUs.'''    )
																try:
																				start_processes(lowerCamelCase  ,     args=lowerCamelCase  ,     nprocs=lowerCamelCase  ,     start_method="""fork"""    )
																except ProcessRaisedException as e:
																				if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																								raise RuntimeError(
																								    """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
																								    """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
																								    """Please review your imports and test them when running the `notebook_launcher()` to identify """
																								    """which one is problematic."""    ) from e

								else:
												# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
												if is_mps_available():
																UpperCamelCase_:				List[Any]						=    """1"""
																print("""Launching training on MPS."""    )
												elif torch.cuda.is_available():
																print("""Launching training on one GPU."""    )
												else:
																print("""Launching training on CPU."""    )
												function(*lowerCamelCase    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=()  ,     lowerCamelCase=2    )	->						List[str]:
				from torch.multiprocessing import start_processes

				with tempfile.NamedTemporaryFile() as tmp_file:
								# torch.distributed will expect a few environment variable to be here. We set the ones common to each
								# process here (the other ones will be set be the launcher).
								with patch_environment(
								    world_size=lowerCamelCase  ,     master_addr="""127.0.01"""  ,     master_port="""29500"""  ,     accelerate_mixed_precision="""no"""  ,     accelerate_debug_rdv_file=tmp_file.name  ,     accelerate_use_cpu="""yes"""  ,     ):
												UpperCamelCase_:				str						=    PrepareForLaunch(lowerCamelCase  ,     debug=lowerCamelCase    )
												start_processes(lowerCamelCase  ,     args=lowerCamelCase  ,     nprocs=lowerCamelCase  ,     start_method="""fork"""    )






